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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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N-gram and Word2Vec Feature Engineering Approaches for Spam Recognition on Some Influential Twitter Topics in Saudi Arabia 22

N-gram and Word2Vec Feature Engineering Approaches for Spam ...

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6th International Conference on information system and Data Mining, ICISDM 2022

作者： Balfagih, Ahmed Keselj, Vlado Taylor, Stacey Faculty of Computer Science Dalhousie University Canada Information System Faculty of Computing and Information Technology King Abdulaziz University Saudi Arabia

ISBN: (纸本)9781450396257

Social media platforms, such as Twitter, have become powerful sources of information on people's perception of major events. Many people use Twitter to express their views on various issues and events and use it to develop their opinion on the diverse economic, political, technical, and social occurrences related to their daily lives. Spam and non-relevant tweets are a major challenge for Twitter trend detection. Saudi Arabia is a top ranked country in Twitter usage worldwide, and in recent years has experienced difficulties due to the use and rise of hashtags based on misleading tweets and spam. The goal of this paper is to apply machine learning techniques to identify spam on the Saudi tweets collected to the end of 2020. To date, spam detection on Twitter data has been mostly done in English, leaving other major languages, such as Arabic, insufficiently covered. Additionally, publicly accessible Arabic Twitter datasets are hard to find. For our research, we use eight Twitter datasets on some significant topics in politics, health, national affairs, economy, and sport, to train and evaluate different machine learning algorithms, with a focus on two feature generation techniques based on N-grams and Word2Vec embeddings. One contribution of this paper is providing these new labelled datasets with embeddings. The experimental results show improvement from using embeddings over N-grams in more balanced datasets vs. more unbalanced ones. We also find a superior performance of the Random Forest algorithm over other algorithms in most experiments. © 2022 ACM.

关键词： Embeddings

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Range-angle Imaging Based on the Prior of Target Scattering Characteristics in Distributed Arrays 2

Range-angle Imaging Based on the Prior of Target Scattering ...

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2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Mei, Yongjie Zhang, Guangbin Huang, Tianyao Wang, Yan Beijng Institute of Technology School of Information and Electronics Beijing China Yangtze Delta Region Academy Beijing Institute of Technology Radar System and Intelligent Signal Processing Jiaxing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

We consider the problem of target range-angle imaging using distributed arrays. High resolution imaging requires a large array aperture. However, due to the limited number of array elements, the subarrays are usually sparsely located, which results in severe under-sampling and ambiguity problem, degrading the imaging performance. To this end, we propose a range-angle imaging method based on the prior of target scattering characteristic to resolve the ambiguity. The main idea is to use the information increment of priors to reduce the dependence on the measurements. Particularly, we first statistically extract the prior distribution of target scattering intensity by data analysis on the target images. Then we use these priors to design weighting parameters and achieve the ambiguity-free images by solving a weighted sparse recovery problem. Finally, numerical simulation results verify the feasibility of the proposed method. © 2024 IEEE.

关键词： ambiguity distributed array Range-angle imaging the prior of target scattering characteristics weight

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Semantic-based Attention model for Hospital Readmission Prediction

Semantic-based Attention model for Hospital Readmission Pred...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Im, Sea Jung Xu, Yue Watson, Jason Queensland University of Technology School of Computer Science Brisbane Australia Queensland University of Technology School of Information System Brisbane Australia

ISBN: (纸本)9781665480451

Unexpected hospital readmissions are problematic to both hospitals and patients, and also costly in terms of money and resources as well. Thus, identifying whether or not a patient will be readmitted becomes an important task. Due to the limitation of traditional machine learning techniques, Recurrent neural networks (RNN) and attention mechanisms have been proposed to learn temporal relationships between patient admissions for readmission prediction. Especially, learning accurate representations to represent medical concepts plays a crucial role for generating accurate predictions. Existing works demonstrate that incorporating medical ontologies can be beneficial to learn accurate medical code representations. However, current works ignore the importance of the textual descriptions associated with the medical codes in medical ontologies. In fact, these textual descriptions provide valuable information about the meaning of the medical codes and also the semantic relations among them. In this paper, we propose a new model called Semantic-based Attention model for Medical Concept Embedding (SAMCE). It takes into consideration the semantic information provided in code descriptions to learn medical code representations based on which to generate readmission prediction via RNN and attention mechanisms. Experimental results show that the proposed SAMCE model improves not only the performance of readmission prediction but also the quality of medical code representations. © 2022 IEEE.

关键词： Semantics

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Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

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Frontiers of Earth Science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

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Utilizing Genetic Algorithms for Detecting Congenital Heart Defects

Utilizing Genetic Algorithms for Detecting Congenital Heart ...

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2024 International Conference on E-Mobility, Power Control and Smart systems, ICEMPS 2024

作者： Patil, Sachin C. Kasula, Balaram Yadav Mohammed, Vazeer Ali Gupta, Ketan Thamaraimanalan, T. United States University of The Cumberlands Dept of Information Technology WilliamsburgKY United States Lewis University Dept. of Computer & Information System IL United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore India

ISBN: (纸本)9798350394399

Genetic Algorithms have been more and more employed to stumble on congenital heart defects in the last variety of years. Gasoline is a computational technique that uses evolutionary concepts to optimize answers to complex problems. This study proposes the use of Genetic Algorithms as a novel approach for detecting Congenital Heart Defects a common and potentially life-threatening condition affecting newborns. By incorporating genetic principles and simulations, GAs have proven to be effective in solving complex medical diagnosis problems. The study aims to optimize the classification process by employing evolutionary techniques to identify key features and prioritize them based on their importance in diagnosing CHDs. Results from the GA model showed a significant improvement in accuracy and speed compared to existing methods, making it a promising tool for early detection and intervention of CHDs in newborns. But gas is more appropriate for this assignment because of its potential to pick out extra subtle variations between ordinary and abnormal cardiovascular anatomy. Gas has additionally been established to be promising within the identity and quantification of CHDs. © 2024 IEEE.

关键词： Genetic algorithms

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Code Vulnerability Detection via Nearest Neighbor Mechanism

Code Vulnerability Detection via Nearest Neighbor Mechanism

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Du, Qianjin Kuang, Xiaohui Zhao, Gang Department of Computer Science and Technology Tsinghua University China National Key Laboratory of Science and Technology on Information System Security China

Code vulnerability detection is a fundamental and challenging task in the software security field. Existing research works aim to learn semantic information from the source code by utilizing NLP technologies. However, in vulnerability detection tasks, some vulnerable samples are very similar to non-vulnerable samples, which are difficult to identify. To address this issue and improve detection performance, we introduce the k-nearest neighbor mechanism which retrieves multiple neighbor samples and utilizes label information of retrieved neighbor samples to provide help for model predictions. Besides, we use supervised contrastive learning to make the model learn the discriminative representation and ensure that label information of retrieved neighbor samples is as consistent as possible with the label information of testing samples. Extensive experiments show that our method can achieve obvious performance improvements compared to baseline models. © 2022 Association for Computational Linguistics.

关键词： Semantics

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An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

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computer systems Science & Engineering 2022年第12期43卷 1175-1188页

作者： Norwini Zaidi Iskandar Ishak Fatimah Sidi Lilly Suriani Affendey System Development and Engineering Center Universiti Sains Islam Malaysia71800Negeri SembilanMalaysia Faculty of Computer Science and Information Technology Universiti Putra MalaysiaUPM Serdang43400SelangorMalaysia

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However,there is no standard guideline for data transformation from relational database toNoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processingtime when compared to the relational database query processing time. However,these approaches produced redundant tables in the resulted schema that in turnconsume large unnecessary storage size and produce high query processing timedue to the generated schema with redundant column families in the transformedcolumn-oriented database. In this paper, an efficient data transformation techniquefrom relational database to column-oriented database is proposed. The proposedschema transformation technique is based on the combination of denormalizationapproach, data access pattern and multiple-nested schema. In order to validate theproposed work, the proposed technique is implemented by transforming data fromMySQL database to MongoDB database. A benchmark transformation techniqueis also performed in which the query processing time and the storage size arecompared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storagespace usage due to the reduced number of column families in the column-orienteddatabase.

关键词： Data migration data transformation column-oriented database relational database big data

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Continual Learning with a Memory of Non-similar Samples 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Min, Qilang He, Juanjuan Yang, Liuyan Fu, Yue College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

The replay method is an effective strategy to address the problem of catastrophic forgetting, a key challenge for continuous learning. However, the sample sets obtained by replay-based methods generally suffer from local data information deficiencies. This problem leads to an imbalance in the plasticity-stability of the model on older tasks. This paper proposes a novel method, called non-similar sample storage (NSS). Non-similar refers to the Euclidean distance for feature vectors of different samples being far. NSS extracts the feature vectors of the samples and then calculates the similarity of the feature vectors for each sample after the current task training. Samples that contribute less to the model classification effect among similar samples are iteratively deleted, and the subset of low-similarity samples is retained. Moreover, NSS reserves 30% of the storage space for saving samples near the center of the sample set. Low-similarity samples stored by NSS get larger losses during the replay process, leading to lower training effectiveness of the current task. This paper introduces a knowledge distillation strategy to solve this problem. A variable parameter was used to balance the classification loss of the new task with the distillation loss of the old task (NSS-D). Experimental results in CIFAR10 and imbalanced CIFAR10 show that NSS maximizes the data’s global information and can retain the model’s ability to recognize old tasks better. Compared with classical algorithms, NSS-D performs better on CIFAR100 (48.8%) and ImageNet-200 (36.7%). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Distillation

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